brain-score.org submission (user:672) | (public:False)

brainscore_vision/models/resnet50_st_wzc/__init__.py

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+from brainscore_vision import model_registry
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from .model import get_model, get_layers
+
+model_registry['resnet50_st_wzc'] = lambda: ModelCommitment(identifier='resnet50_st_wzc', activations_model=get_model('resnet50_st_wzc'), layers=get_layers('resnet50_st_wzc'))

brainscore_vision/models/resnet50_st_wzc/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+import numpy as np
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+import torchvision.models
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+
+# This is an example implementation for submitting resnet-50 as a pytorch model
+
+# Attention: It is important, that the wrapper identifier is unique per model!
+# The results will otherwise be the same due to brain-scores internal result caching mechanism.
+# Please load your pytorch model for usage in CPU. There won't be GPUs available for scoring your model.
+# If the model requires a GPU, contact the brain-score team directly.
+
+from brainscore_vision.model_helpers.s3 import load_file
+device = torch.device("cpu")
+
+def get_model_list():
+    return ['resnet50_st_wzc']
+
+def get_model(name):
+    assert name == 'resnet50_st_wzc'
+
+    file_path = load_file(bucket="brainscore-storage", folder_name="brainscore-vision/models/user_672/",
+                          relative_path="resnet50_st_wzc_checkpoint_89.pth",
+                          version_id="pvRIJym8f_PtCaijOUDaKv3xJi64jYtw",
+                          ),
+    checkpoint = torch.load(file_path, map_location=lambda storage, loc: storage)
+    new_state_dict = {}
+    for key, value in checkpoint["state_dict"].items():
+        new_key = key.replace('module.', '', 1)
+        new_state_dict[new_key] = value
+
+    model = torchvision.models.__dict__["resnet50"]()
+    model.load_state_dict(new_state_dict, strict=True)
+    model.to(device)
+    model.eval()
+    # model = torchvision.models.resnet50(pretrained=True)
+    preprocessing = functools.partial(load_preprocess_images_change, image_size=224)
+    wrapper = PytorchWrapper(identifier='resnet50_st_wzc', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+# def get_layers(name):
+#     assert name == 'resnet50_st_wzc'
+    #return ['conv1','layer1', 'layer2', 'layer3', 'layer4', 'fc']
+def get_layers(name):
+    assert name == 'resnet50_st_wzc'
+    layer_names = (['conv1'] + [f'layer1.{i}' for i in range(3)] +
+                   [f'layer2.{i}' for i in range(4)] +
+                   [f'layer3.{i}' for i in range(6)] +
+                   [f'layer4.{i}' for i in range(3)] + ['avgpool'])
+    return layer_names
+
+
+def get_bibtex(model_identifier):
+    return """"""
+
+#--------------------------------------------------------------------
+
+
+def load_preprocess_images_change(image_filepaths, image_size, **kwargs):
+    images = [load_image(image_filepath) for image_filepath in image_filepaths] #load_images(image_filepaths)
+    images = preprocess_images_change(images, image_size=image_size, **kwargs)
+    return images
+
+def load_image(image_filepath):
+    with Image.open(image_filepath) as pil_image:
+        if 'L' not in pil_image.mode.upper() and 'A' not in pil_image.mode.upper() \
+                and 'P' not in pil_image.mode.upper():  # not binary and not alpha and not palletized
+            # work around to https://github.com/python-pillow/Pillow/issues/1144,
+            # see https://stackoverflow.com/a/30376272/2225200
+            return pil_image.copy()
+        else:  # make sure potential binary images are in RGB
+            rgb_image = Image.new("RGB", pil_image.size)
+            rgb_image.paste(pil_image)
+            return rgb_image
+
+def preprocess_images_change(images, image_size, **kwargs):
+    preprocess = transforms.Compose([
+            transforms.Resize((image_size, image_size)),
+            transforms.ToTensor(),
+            lambda img: img.unsqueeze(0)
+        ])
+    images = [preprocess(image) for image in images]
+    images = np.concatenate(images)
+    return images
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/resnet50_st_wzc/region_layer_map/resnet50_st_wzc.json

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+{
+    "V1": "layer1.0",
+    "V2": "layer2.0",
+    "V4": "layer3.0",
+    "IT": "layer4.2"
+}

brainscore_vision/models/resnet50_st_wzc/requirements.txt

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+torchvision
+torch

brainscore_vision/models/resnet50_st_wzc/test.py

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+import pytest
+import brainscore_vision
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('resnet50_st_wzc')
+    assert model.identifier == 'resnet50_st_wzc'